MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum mechanical methods, machine learning, and their combinations. We are happy to announce that we will release the new upgraded version of MLatom 3.3.0 that …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more »
MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide variety of representative potentials. These potentials include: You can train and optimize the hyperparameters of these machine learning potentials using flexible options. We also …
Training and using machine learning potentials with MLatom@XACS Read more »
We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release notes for details. How to get it? Download zip, check this version on PyPI and GitHub. pip install mlatom==3.2.0 What’s new? This is a major release with many new features, …
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the …
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We have developed a robust protocol and demonstrated its feasibility for calculating stimulated emission contributions in transient absorption pump–probe and 2D electronic spectra of pyrazine. …
The capabilities of MLatom 3 are described in the paper published in the J. Chem. Theory Comput. which was converted into an interactive online manual. See also the video overview.
Our editorial on the Special Topic “Modern semiempirical methods” was published in JCP. We overview contributions and overview the trends in the development and applications of semiempirical quantum mechanical methods. The contributions include improvements in the formalism of the method …
Editorial on the Special Topic “Modern semiempirical methods” published in JCP Read more »
Location: Xiamen University, China Duration: 2 years (with possible 1-year extension) Xiamen University is offering a fully-funded 2-year postdoctoral research position in the field of machine learning-based simulations of condensed matter. This opportunity is available within the research group of …
Postdoc in Machine Learning-Based Simulations of Condensed Matter Read more »
The new MLatom 3 release comes with the versatile Python API. We are happy to announce the release of its documentation which is available at http://mlatom.com/docs.
The 2nd edition of the International Symposium on Machine Learning in Quantum Chemistry, SMLQC 2023, is approaching and will be held from November 29 to December 1 in Uppsala, Sweden. See the symposium’s website https://www.smlqc2023.com/ for more information. The deadline …
SMLQC 2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry Read more »